A paradox approach to detect internal defects of a production part

Muaaz Abdul Hadi; Milot Gashi; Vignesh Manjunath; Stefan Trabesinger; Johannes Schmid

A paradox approach to detect internal defects of a production part

Muaaz Abdul Hadi^* (Erfinder), Milot Gashi (Erfinder), Vignesh Manjunath (Erfinder), Stefan Trabesinger (Erfinder), Johannes Schmid (Erfinder)

^*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Schutzrecht › Patent

Abstract

The unique approach detects the defects/anomalies in a production part during its manufacturing (machining, grinding, etc.,) phase. This is done via the machine generated High Frequency Data (HF-Data) and the energy consumption plot. The specific defect/anomaly detected part will then be tested at the state-of-the-art (usual) end-of-line (EOL) testbed.

Originalsprache	englisch
Publikationsstatus	Eingereicht - 27 Juni 2022

Fields of Expertise

Mobility & Production

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

2 Artikel

High-frequency machine datasets captured via Edge Device from Spinner U5-630 milling machine
Abdul Hadi, M., Brillinger, M., Trabesinger, S. & Schmid, J., 3 Dez. 2021, in: Data in Brief. 39, C, 5 S., 107670.
Publikation: Beitrag in einer Fachzeitschrift › Artikel › Begutachtung

Open Access
Sustainable peak power smoothing and energy-efficient machining process thorough analysis of high-frequency data
Abdul Hadi, M., Brillinger, M., Wuwer, M., Schmid, J., Trabesinger, S., Jäger, M. & Haas, F., 10 Okt. 2021, in: Journal of Cleaner Production. 318, 11 S., 128548.
Publikation: Beitrag in einer Fachzeitschrift › Artikel › Begutachtung

Open Access

Dieses zitieren

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AU - Abdul Hadi, Muaaz

AU - Gashi, Milot

AU - Manjunath, Vignesh

AU - Trabesinger, Stefan

AU - Schmid, Johannes

PY - 2022/6/27

Y1 - 2022/6/27

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AB - The unique approach detects the defects/anomalies in a production part during its manufacturing (machining, grinding, etc.,) phase. This is done via the machine generated High Frequency Data (HF-Data) and the energy consumption plot. The specific defect/anomaly detected part will then be tested at the state-of-the-art (usual) end-of-line (EOL) testbed.

M3 - Patent

ER -

A paradox approach to detect internal defects of a production part

Abstract

Fields of Expertise

UN SDGs

Fingerprint

Publikationen

High-frequency machine datasets captured via Edge Device from Spinner U5-630 milling machine

Sustainable peak power smoothing and energy-efficient machining process thorough analysis of high-frequency data

Dieses zitieren